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    AI Coding Prompt Refiner for Better Developer Results

    6

    Transforms vague coding requests into precise, scoped, testable, AI-ready prompts for Cursor, Claude Code, Codex CLI, Replit, and other coding agents.

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    See it in action

    You say

    Improve this prompt and make it safe for Cursor:

    “Add a dashboard to my SaaS app.”

    The app already has login. I want the dashboard to show user metrics, recent activity, and quick actions. Make sure the AI does not rewrite unrelated files or break authentication.

    Your agent does

    === CURSOR-READY PROMPT ===

    Task: Build a responsive dashboard page for authenticated users in the existing SaaS app.

    Context: The app already has login/authentication. The dashboard should show user metrics, recent activity, and quick actions while following existing project patterns.

    Before editing:

    1. Inspect the project structure.
    2. Identify the framework, routing system, authentication flow, layout components, styling conventions, and existing data-fetching patterns.
    3. Identify whether a dashboard route or authenticated layout already exists.
    4. Identify any existing metric, activity, card, button, or navigation components that should be reused.
    5. Check for existing tests and run relevant ones if possible.
    6. Create a brief implementation plan before editing.

    Scope: Create or update the authenticated dashboard experience.

    Out of scope:

    • changing authentication logic
    • changing database schema
    • changing payment or billing logic
    • replacing the styling system
    • rewriting unrelated components
    • changing public routes
    • adding unnecessary dependencies

    Implementation requirements: The dashboard must include:

    1. Welcome/header section for the signed-in user.
    2. User metrics section with clear cards.
    3. Recent activity section.
    4. Quick actions section.
    5. Loading state.
    6. Empty state for new users.
    7. Error state for failed data loading.
    8. Responsive layout for desktop, tablet, and mobile.
    9. Accessible headings, labels, buttons, and color contrast.

    Data requirements: Use existing API/data-fetching patterns if available. If real dashboard data is not available yet, create a clearly marked mock-data layer or placeholder structure that can be replaced later without changing the UI.

    Acceptance criteria:

    • Authenticated users can view the dashboard.
    • Unauthenticated access follows the existing app behavior.
    • Dashboard layout works on mobile and desktop.
    • Loading, empty, error, and success states are handled.
    • Existing navigation and authentication behavior remain unchanged.
    • No unrelated broad refactor is performed.
    • Code follows existing project conventions.

    Testing requirements:

    • Run existing tests if available.
    • Add or recommend component/page tests for dashboard states if the project supports them.
    • Manually verify the dashboard at mobile and desktop widths.
    • Verify no console errors appear.
    • Verify authentication flow is not changed.

    Constraints:

    • Do not rewrite unrelated files.
    • Do not modify authentication logic unless required and explained first.
    • Do not change database schema.
    • Do not add new dependencies unless necessary.
    • Do not expose secrets or environment variable values.

    Return:

    • files inspected
    • files changed
    • implementation summary
    • tests added or run
    • manual verification steps
    • assumptions made
    • risks remaining

    What you get

    Convert vague feature ideas into scoped implementation briefsTurn bug reports into structured debugging prompts with root-cause analysisGenerate safe refactoring instructions that protect sensitive logicAutomatically add unit testing requirements to any AI coding taskAudit existing prompts to identify safety risks and context gapsBuild test-writing prompts for fragile modulesImprove Claude Code prompts before editing a repository

    About this skill

    AI Coding Prompt Refiner helps developers, beginners, founders, students, indie hackers, no-code builders, and AI coding users turn vague coding requests into precise, structured, implementation-ready prompts. It improves weak prompts such as “fix my app,” “add login,” “refactor this,” or “make it production-ready” by adding project context, scope boundaries, repository inspection steps, constraints, acceptance criteria, testing requirements, safety notes, and final response expectations. The skill creates optimized prompts for Cursor, Claude Code, Codex CLI, OpenCode, Replit, ChatGPT Agents, and generic AI coding assistants, helping users get safer, clearer, and more reliable coding results.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Listed3 months ago
    Updated17 days ago

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